advanced analytics

What is advanced analytics?

Advanced analytics is a data analysis methodology using predictive modeling, machine learning algorithms, deep learning, business process automation and other statistical methods to analyze business information from a variety of data sources.

Advanced analytics uses data science beyond traditional business intelligence (BI) methods to predict patterns, estimate the likelihood of future events and find insights in data that experts might miss. Predictive analytics capabilities can help an organization be more efficient and increase its accuracy in decision-making.

Data scientists often use advanced analytics tools to combine prescriptive analytics and predictive analytics. Using different analytics types together adds options for enhanced visualization and predictive models.

Why is advanced analytics important?

Advanced analytics is a valuable resource because it enables an organization to improve data asset functionality, regardless of where the data is stored or what format it's in. Advanced analytics can also help address some of the more complex business problems traditional BI reporting cannot.

For example, to create a contextual marketing engine, a consumer-packaged goods manufacturer might need to ask the following questions:

• When is a customer likely to exhaust their supply of an item?
• What time of the day or week are they most receptive to marketing advertisements?
• What level of profitability is achievable when marketing at the time?
• What price point are they most likely to purchase at?

Organizations can combine consumption models with historical data and artificial intelligence (AI), which enables advanced analytics to determine precise answers to the previous questions, and better understand their customers.

What are the benefits of advanced analytics?

In addition to enabling more efficient use of data assets and providing decision-makers with higher confidence in data accuracy, advanced analytics offers the following benefits:

• Accurate forecasting. Using advanced analytics can confirm or refute prediction and forecast models with better accuracy than traditional BI tools, which still carry an element of uncertainty.
• Faster decision-making. Improving the accuracy of predictions allows executives to act more quickly. They can be confident their quicker business decisions will achieve the desired results and favorable outcomes can be repeated.
• Deeper insight. Advanced analytics offers a deeper level of actionable insight from data, including customer preference, market trends and key business processes. Better insights empower stakeholders to make data-driven decisions with direct effects on their strategy.
• Improved risk management. The higher level of accuracy advanced analytics provides predictions can help businesses reduce their risk of costly mistakes.
• Anticipate problems and opportunities. Advanced analytics uses statistical models to reveal potential problems on the business's current trajectory or identify new opportunities. Stakeholders can quickly change course and achieve better outcomes.

What are some advanced analytics techniques?

Advanced analytics can help provide organizations with a competitive advantage. Techniques range from basic statistical or trend analysis to more complex tasks requiring BI or specialized tools. The most complex techniques can handle big data, apply machine learning techniques and perform complex tasks. Some commonly used advanced analytics techniques include the following:

Data mining. The data mining process sorts through large data sets to identify patterns and establish relationships. It's a key part of successful analytics operations because BI and advanced analytics applications use the data that mining generates to solve problems. It has applications across a variety of industries including healthcare, government, scientific research, mathematics and sports.

Sentiment analysis. At its core, sentiment analysis is about understanding emotions. It processes text data to determine the attitude or emotion behind the words, which can be positive, negative or neutral. In a business setting, sentiment analysis can help the business to understand how customers feel about a brand based on their reviews, social media comments or direct feedback. Tools used for sentiment analysis range from basic text analytics software to more advanced natural language processing (NLP) tools, some of which use machine learning to improve accuracy.

Cluster analysis. Cluster analysis is a method of grouping. It brings together similar items in a data set. Data groups, or clusters, contain items more similar to each other than items in other clusters. For example, a telecom company could use cluster analysis to group customers based on their usage patterns. Then, they can target each group with a specific marketing strategy.

Complex event processing. Complex event processing (CEP) involves analyzing multiple events happening across various systems in real time to detect patterns. If CEP detects patterns of interest or abnormal behaviors, it can trigger alerts for immediate action. A practical example is credit card fraud detection: The system monitors transactions and flags any suspicious patterns for investigation.

Recommender systems. Recommender systems use past behavior analysis to predict what a user might want, and then personalize suggestions. An everyday example is when an online shopping site suggests products a customer might prefer based on their browsing history, or when a streaming service suggests a show the user may want to watch next.

Time series analysis. Time series analysis focuses on data changes over time. It looks at patterns, trends and cycles in the data to predict future points. For instance, a retailer might use time series analysis to forecast future sales based on past sales data. The results can help the retailer plan stock levels and manage resources efficiently.

Big data analytics. Big data analytics is the process of examining large volumes of structured, semistructured and unstructured data to uncover information such as hidden patterns, correlations, market trends and customer preferences. It uses analytics systems to power predictive models, statistical algorithms and what-if analysis.

Machine learning. The development of machine learning has dramatically increased the speed of data processing and analysis, facilitating disciplines such as predictive analytics. Machine learning uses AI to enable software applications to predict outcomes more accurately. The inputs use historical data to predict new outputs. Common use cases include recommendation engines, fraud detection and predictive maintenance.

Data visualization. Data visualization is the process of presenting data in graphical format. It makes data analysis and sharing more accessible across organizations. Data scientists use visualizations after writing predictive analytics or machine learning algorithms to visualize outputs, monitor results and ensure models perform as intended. It's also a quick and effective way to communicate information to others.

What are some use cases for advanced analytics?

The following examples show how business processes can benefit from advanced analytics software:

• Marketing metrics. With advanced analytics, marketing organizations can create customized, targeted marketing campaigns and avoid wasting money on ineffective strategies. Analyzing future outcomes also can help an organization identify opportunities to up-sell and optimize the marketing funnel.
• Supply chain optimization. Advanced analytics can help an organization factor demand, cost fluctuations and changing consumer preferences to create an agile supply chain which can quickly adapt to changing market conditions.
• Risk management. Advanced analytics can examine particular data sets and data streams in real time. Data scientists can use the results to identify potential high-risk-level patterns such as possible payment fraud or insurance liabilities.
• Business operations. Advanced analytics can help organizations streamline and adapt their operations to better suit predictions on changing market conditions or trends and ultimately increase revenue.

Implementing advanced analytics

Advanced analytics implementation begins with a well-crafted plan. The key to success is not just choosing the right tools, but also building a team with the right skills. The decision to train existing employees or hire new ones is a crucial part of the strategic plan.

Training current BI users is a cost-effective approach to ensure continuity, but it requires a significant time investment. Numerous online courses can help users improve their skills. Existing employees likely know the business and its data; they already understand the business context, which can prove invaluable in analytics. One downside is how much time training can take, especially for more advanced techniques. It requires patience and commitment.

Hiring new staff brings in advanced skills quickly, but it costs more -- data scientists are expensive to hire now. And hiring new staff can pose integration challenges: New staff with specialized skills bring immediate access to advanced capabilities, but they'll need time to understand the nuances of the business and its data.

A mix of skills is essential in an analytics team. Each data team needs people who can understand the data, interpret the analysis and translate insights into business strategies. Perhaps one or two strategic hires can integrate effectively into an existing team and help bring team members up to speed.

Fostering a data-driven culture is important. Everyone in an organization -- specialist or not -- should understand the value of data and feel empowered to use it in their decisions. Empowering users enables a business to unlock the potential of advanced analytics. Investment in advanced analytics is not just about technology, but also about people and culture. It's just as important to build a team with the right skills and encourage a data-driven culture as it is to choose the right tools.

Evaluating advanced analytics tools

Organizations can choose from several advanced analytics platforms. Each offers different advantages, depending on the use case. Advanced analytics tools have two categories: open source and proprietary.

Open source tools

Open source tools have become a go-to option for many data scientists doing machine learning and prescriptive analytics. They include programming languages, as well as computing environments, such as Hadoop and Spark. Data scientists typically like open source advanced analytics tools thanks to their inexpensive price tags. Open source analytics tools also offer strong functionality and administrators can access support from the user community which continuously updates the tools.

Proprietary tools

On the proprietary side, vendors such as Microsoft, IBM and the SAS Institute all offer advanced analytics tools. Most require a deep technical background and understanding of mathematical techniques.

Self-service analytics tools have matured to make functionality more accessible to business users with offerings from vendors such as Alteryx, Qlik, Sisense and Tableau.